{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### １．雨量数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flood_precip</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flood_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-08-06 08:00:00</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-06 09:00:00</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-06 10:00:00</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-06 11:00:00</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     flood_precip\n",
       "flood_date                       \n",
       "2018-08-06 08:00:00           0.0\n",
       "2018-08-06 09:00:00           0.0\n",
       "2018-08-06 10:00:00           0.0\n",
       "2018-08-06 11:00:00           0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precipitation = pd.read_excel('XAJ_data_demo.xlsx', header=None, index_col=0)\n",
    "\n",
    "precipitation.columns = ['flood_precip'] # 设置列名称\n",
    "\n",
    "precipitation.index = precipitation.index.set_names(['flood_date']) # 设置index名字\n",
    "precipitation[:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ２．流量数据整理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "discharge_1 = pd.read_excel('discharge_demo.xls', index_col='TM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TM</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-08-06 08:00:00</th>\n",
       "      <td>757.486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-06 09:00:00</th>\n",
       "      <td>744.343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-06 10:00:00</th>\n",
       "      <td>720.686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-06 11:00:00</th>\n",
       "      <td>710.171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Q\n",
       "TM                          \n",
       "2018-08-06 08:00:00  757.486\n",
       "2018-08-06 09:00:00  744.343\n",
       "2018-08-06 10:00:00  720.686\n",
       "2018-08-06 11:00:00  710.171"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discharge = pd.read_excel('discharge_demo.xls', index_col='TM')\n",
    "# 按每５分钟重采样并对NaN数据进行前向填充\n",
    "discharge = discharge.resample('5min').asfreq().fillna(method='ffill')\n",
    "discharge = discharge.resample('h').asfreq()  # 取整点数据\n",
    "discharge[:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ３．洪峰选取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: Qt5Agg\n"
     ]
    }
   ],
   "source": [
    "%matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "runoff = discharge['20180807 03:00': '20180807 18:00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取头尾两个流量\n",
    "temp = runoff.drop(runoff.index[1:runoff.shape[0]-1])\n",
    "# 生成基流\n",
    "discharge_basic = temp.resample('h').interpolate(method='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2 entries, 2018-08-07 03:00:00 to 2018-08-07 18:00:00\n",
      "Data columns (total 1 columns):\n",
      "Q    2 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 112.0 bytes\n"
     ]
    }
   ],
   "source": [
    "temp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(discharge.index, discharge.values)\n",
    "plt.plot(discharge_basic.index, discharge_basic.values)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ee0e66208>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "flood = runoff - discharge_basic\n",
    "flood.columns = ['flood_quant']\n",
    "flood.index = flood.index.set_names('flood_date')\n",
    "flood.plot(grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 合并流量和雨量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "flood_p = pd.merge(precipitation, flood, how='left', left_index=True, right_index=True).fillna(0)\n",
    "# flood_p.plot(grid=True)\n",
    "\n",
    "flood_p['flood_date'] = flood_p.index\n",
    "flood_p['flood_id'] = '20180807'\n",
    "flood_p['flood_evapor'] = 0\n",
    "flood_p.reset_index(drop=True, inplace=True)\n",
    "flood_p = flood_p[['flood_id', 'flood_date', 'flood_precip', 'flood_quant', 'flood_evapor']]\n",
    "flood_p['flood_quant'] = flood_p['flood_quant'].map('{:.1f}'.format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yasin/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py:3697: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flood_id</th>\n",
       "      <th>day_date</th>\n",
       "      <th>day_precip</th>\n",
       "      <th>day_evapor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-06 08:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-06 09:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-06 10:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-06 11:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   flood_id            day_date  day_precip  day_evapor\n",
       "0  20180807 2018-08-06 08:00:00         0.0           0\n",
       "1  20180807 2018-08-06 09:00:00         0.0           0\n",
       "2  20180807 2018-08-06 10:00:00         0.0           0\n",
       "3  20180807 2018-08-06 11:00:00         0.0           0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_rain = flood_p[:11]\n",
    "pre_rain[:4]\n",
    "\n",
    "pre_rain.drop(columns='flood_quant', inplace=True)\n",
    "pre_rain.columns = ['flood_id', 'day_date', 'day_precip', 'day_evapor']\n",
    "pre_rain.to_csv('pre_rain.txt', sep='\\t', index=None)\n",
    "pre_rain[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "flood_p = flood_p[11:]\n",
    "flood_p.to_csv('flood.txt', sep='\\t', index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre_rain.shape: (11, 4)\n",
      "flood_p.shape: (44, 5)\n"
     ]
    }
   ],
   "source": [
    "print('pre_rain.shape: {}\\nflood_p.shape: {}'.format(pre_rain.shape, flood_p.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 可视化验证数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flood_id</th>\n",
       "      <th>flood_date</th>\n",
       "      <th>flood_precip</th>\n",
       "      <th>flood_quant</th>\n",
       "      <th>flood_evapor</th>\n",
       "      <th>simulated_flow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-06 19:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-06 20:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-06 21:00:00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    flood_id          flood_date  flood_precip  flood_quant  flood_evapor  \\\n",
       "11  20180807 2018-08-06 19:00:00           0.0          0.0             0   \n",
       "12  20180807 2018-08-06 20:00:00           0.0          0.0             0   \n",
       "13  20180807 2018-08-06 21:00:00           0.5          0.0             0   \n",
       "\n",
       "    simulated_flow  \n",
       "11             0.0  \n",
       "12             0.0  \n",
       "13             0.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('simulated_flow.pickle', 'rb') as f:\n",
    "    simulated_flow = pickle.load(f)\n",
    "\n",
    "flood_p['simulated_flow'] = simulated_flow[0]\n",
    "\n",
    "\n",
    "flood_p['flood_quant'] = flood_p['flood_quant'].astype(float)\n",
    "flood_p['simulated_flow'] = flood_p['simulated_flow'].astype(float)\n",
    "\n",
    "flood_p = flood_p.round({'flood_quant': 1, 'simulated_flow': 1})\n",
    "flood_p[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(flood_p['flood_date'], flood_p['flood_quant'])\n",
    "plt.plot(flood_p['flood_date'], flood_p['simulated_flow'])\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flood_id</th>\n",
       "      <th>flood_date</th>\n",
       "      <th>flood_precip</th>\n",
       "      <th>flood_quant</th>\n",
       "      <th>flood_evapor</th>\n",
       "      <th>simulated_flow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-06 19:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-06 20:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-06 21:00:00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-06 22:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-06 23:00:00</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 00:00:00</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 01:00:00</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 02:00:00</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 03:00:00</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 04:00:00</td>\n",
       "      <td>36.9</td>\n",
       "      <td>53.8</td>\n",
       "      <td>0</td>\n",
       "      <td>55.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 05:00:00</td>\n",
       "      <td>50.9</td>\n",
       "      <td>557.8</td>\n",
       "      <td>0</td>\n",
       "      <td>113.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 06:00:00</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1041.6</td>\n",
       "      <td>0</td>\n",
       "      <td>192.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 07:00:00</td>\n",
       "      <td>5.2</td>\n",
       "      <td>577.7</td>\n",
       "      <td>0</td>\n",
       "      <td>479.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 08:00:00</td>\n",
       "      <td>3.3</td>\n",
       "      <td>445.5</td>\n",
       "      <td>0</td>\n",
       "      <td>555.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 09:00:00</td>\n",
       "      <td>1.3</td>\n",
       "      <td>450.2</td>\n",
       "      <td>0</td>\n",
       "      <td>499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 10:00:00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>492.7</td>\n",
       "      <td>0</td>\n",
       "      <td>360.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 11:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>457.3</td>\n",
       "      <td>0</td>\n",
       "      <td>227.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 12:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>341.3</td>\n",
       "      <td>0</td>\n",
       "      <td>134.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 13:00:00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>246.1</td>\n",
       "      <td>0</td>\n",
       "      <td>82.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 14:00:00</td>\n",
       "      <td>1.3</td>\n",
       "      <td>157.5</td>\n",
       "      <td>0</td>\n",
       "      <td>256.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 15:00:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>105.6</td>\n",
       "      <td>0</td>\n",
       "      <td>221.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 16:00:00</td>\n",
       "      <td>0.7</td>\n",
       "      <td>60.4</td>\n",
       "      <td>0</td>\n",
       "      <td>236.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 17:00:00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>28.5</td>\n",
       "      <td>0</td>\n",
       "      <td>176.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 18:00:00</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>121.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 19:00:00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 20:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>126.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 21:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>76.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 22:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-07 23:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-08 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-08 01:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-08 02:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-08 03:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-08 04:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-08 05:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-08 06:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-08 07:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-08 08:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-08 09:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-08 10:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-08 11:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-08 12:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-08 13:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>20180807</td>\n",
       "      <td>2018-08-08 14:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    flood_id          flood_date  flood_precip  flood_quant  flood_evapor  \\\n",
       "11  20180807 2018-08-06 19:00:00           0.0          0.0             0   \n",
       "12  20180807 2018-08-06 20:00:00           0.0          0.0             0   \n",
       "13  20180807 2018-08-06 21:00:00           0.5          0.0             0   \n",
       "14  20180807 2018-08-06 22:00:00           0.0          0.0             0   \n",
       "15  20180807 2018-08-06 23:00:00           1.2          0.0             0   \n",
       "16  20180807 2018-08-07 00:00:00           0.7          0.0             0   \n",
       "17  20180807 2018-08-07 01:00:00           7.2          0.0             0   \n",
       "18  20180807 2018-08-07 02:00:00           3.4          0.0             0   \n",
       "19  20180807 2018-08-07 03:00:00          15.6          0.0             0   \n",
       "20  20180807 2018-08-07 04:00:00          36.9         53.8             0   \n",
       "21  20180807 2018-08-07 05:00:00          50.9        557.8             0   \n",
       "22  20180807 2018-08-07 06:00:00           7.3       1041.6             0   \n",
       "23  20180807 2018-08-07 07:00:00           5.2        577.7             0   \n",
       "24  20180807 2018-08-07 08:00:00           3.3        445.5             0   \n",
       "25  20180807 2018-08-07 09:00:00           1.3        450.2             0   \n",
       "26  20180807 2018-08-07 10:00:00           0.3        492.7             0   \n",
       "27  20180807 2018-08-07 11:00:00           0.0        457.3             0   \n",
       "28  20180807 2018-08-07 12:00:00           0.0        341.3             0   \n",
       "29  20180807 2018-08-07 13:00:00           4.0        246.1             0   \n",
       "30  20180807 2018-08-07 14:00:00           1.3        157.5             0   \n",
       "31  20180807 2018-08-07 15:00:00           2.0        105.6             0   \n",
       "32  20180807 2018-08-07 16:00:00           0.7         60.4             0   \n",
       "33  20180807 2018-08-07 17:00:00           0.3         28.5             0   \n",
       "34  20180807 2018-08-07 18:00:00           1.3          0.0             0   \n",
       "35  20180807 2018-08-07 19:00:00           0.8          0.0             0   \n",
       "36  20180807 2018-08-07 20:00:00           0.0          0.0             0   \n",
       "37  20180807 2018-08-07 21:00:00           0.0          0.0             0   \n",
       "38  20180807 2018-08-07 22:00:00           0.0          0.0             0   \n",
       "39  20180807 2018-08-07 23:00:00           0.0          0.0             0   \n",
       "40  20180807 2018-08-08 00:00:00           0.0          0.0             0   \n",
       "41  20180807 2018-08-08 01:00:00           0.0          0.0             0   \n",
       "42  20180807 2018-08-08 02:00:00           0.0          0.0             0   \n",
       "43  20180807 2018-08-08 03:00:00           0.0          0.0             0   \n",
       "44  20180807 2018-08-08 04:00:00           0.0          0.0             0   \n",
       "45  20180807 2018-08-08 05:00:00           0.0          0.0             0   \n",
       "46  20180807 2018-08-08 06:00:00           0.0          0.0             0   \n",
       "47  20180807 2018-08-08 07:00:00           0.0          0.0             0   \n",
       "48  20180807 2018-08-08 08:00:00           0.0          0.0             0   \n",
       "49  20180807 2018-08-08 09:00:00           0.0          0.0             0   \n",
       "50  20180807 2018-08-08 10:00:00           0.0          0.0             0   \n",
       "51  20180807 2018-08-08 11:00:00           0.0          0.0             0   \n",
       "52  20180807 2018-08-08 12:00:00           0.0          0.0             0   \n",
       "53  20180807 2018-08-08 13:00:00           0.0          0.0             0   \n",
       "54  20180807 2018-08-08 14:00:00           0.0          0.0             0   \n",
       "\n",
       "    simulated_flow  \n",
       "11             0.0  \n",
       "12             0.0  \n",
       "13             0.0  \n",
       "14             2.0  \n",
       "15             1.2  \n",
       "16             5.5  \n",
       "17             6.2  \n",
       "18            24.6  \n",
       "19            29.7  \n",
       "20            55.5  \n",
       "21           113.7  \n",
       "22           192.3  \n",
       "23           479.7  \n",
       "24           555.3  \n",
       "25           499.0  \n",
       "26           360.6  \n",
       "27           227.2  \n",
       "28           134.6  \n",
       "29            82.8  \n",
       "30           256.7  \n",
       "31           221.9  \n",
       "32           236.1  \n",
       "33           176.9  \n",
       "34           121.5  \n",
       "35           141.0  \n",
       "36           126.5  \n",
       "37            76.9  \n",
       "38            47.6  \n",
       "39            30.5  \n",
       "40            20.5  \n",
       "41            14.5  \n",
       "42            10.8  \n",
       "43             8.4  \n",
       "44             6.9  \n",
       "45             5.7  \n",
       "46             4.9  \n",
       "47             4.1  \n",
       "48             3.5  \n",
       "49             2.8  \n",
       "50             2.3  \n",
       "51             1.8  \n",
       "52             1.3  \n",
       "53             1.0  \n",
       "54             0.7  "
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flood_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flood = pd.read_csv('flood.txt', sep='\\t', parse_dates=['flood_date'])\n",
    "\n",
    "with open('simulated_flow.pickle', 'rb') as f:\n",
    "    simulated_flow = pickle.load(f)\n",
    "\n",
    "flood['simulate_flow'] = simulated_flow[0]\n",
    "\n",
    "plt.plot(flood['flood_date'], flood['flood_quant'])\n",
    "plt.plot(flood.flood_date, flood.simulate_flow)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flood = pd.read_csv('flood.txt', sep='\\t', parse_dates=['flood_date'])\n",
    "\n",
    "with open('simulated_flow.pickle', 'rb') as f:\n",
    "    simulated_flow = pickle.load(f)\n",
    "\n",
    "flood['simulate_flow'] = simulated_flow[0]\n",
    "\n",
    "plt.plot(flood['flood_date'], flood['flood_quant'])\n",
    "plt.plot(flood.flood_date, flood.simulate_flow)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flood = pd.read_csv('flood.txt', sep='\\t', parse_dates=['flood_date'])\n",
    "\n",
    "with open('simulated_flow.pickle', 'rb') as f:\n",
    "    simulated_flow = pickle.load(f)\n",
    "\n",
    "flood['simulate_flow'] = simulated_flow[0]\n",
    "\n",
    "plt.plot(flood['flood_date'], flood['flood_quant'])\n",
    "plt.plot(flood.flood_date, flood.simulate_flow)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 参数率定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('optimal_params', 'rb') as f:\n",
    "    optimal_params = pickle.load(f)\n",
    "\n",
    "with open('xaj_params.pickle', 'rb') as f:\n",
    "    xaj_params_1 = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9066705795725403,\n",
       " 12.564727056934904,\n",
       " 68.66895657291167,\n",
       " 0.19740372215321428,\n",
       " 137.196361710203,\n",
       " 0.3164611864485325,\n",
       " 0.01071852837428263,\n",
       " 1.231273275649647,\n",
       " 1.1120431860010427,\n",
       " 0.7996515574112859,\n",
       " 0.9922475149215669,\n",
       " 0.8210713682031088,\n",
       " 0.3165578672418067,\n",
       " 0.7658281632037084,\n",
       " 0.05534309646128027]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimal_params[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "K         0.477\n",
       "WUM      14.135\n",
       "WLM      89.453\n",
       "C         0.131\n",
       "WM      142.912\n",
       "B         0.161\n",
       "IMP       0.025\n",
       "SM       28.736\n",
       "EX        1.413\n",
       "KSS       0.023\n",
       "KKSS      0.097\n",
       "KKG       0.987\n",
       "CR        0.544\n",
       "L         0.154\n",
       "XE        0.180\n",
       "KE        1.001\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xaj_params_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "K         1.261\n",
       "WUM      12.644\n",
       "WLM      65.948\n",
       "C         0.173\n",
       "WM      190.880\n",
       "B         0.354\n",
       "IMP       0.038\n",
       "SM        1.156\n",
       "EX        1.285\n",
       "KSS       0.474\n",
       "KKSS      0.850\n",
       "KKG       0.756\n",
       "CR        0.516\n",
       "L         0.942\n",
       "XE        0.310\n",
       "KE        1.001\n",
       "dtype: float64"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1[:15] = optimal_params[0]\n",
    "x1.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "K         0.998\n",
       "WUM      27.764\n",
       "WLM      84.393\n",
       "C         0.200\n",
       "WM      182.515\n",
       "B         0.400\n",
       "IMP       0.040\n",
       "SM       51.634\n",
       "EX        1.002\n",
       "KSS       0.379\n",
       "KKSS      0.986\n",
       "KKG       0.284\n",
       "CR        0.766\n",
       "L         1.791\n",
       "XE        0.029\n",
       "KE        1.001\n",
       "dtype: float64"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xaj_params"
   ]
  }
 ],
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